Gillby

Stored environmental history

get_history

Read-onlyIdempotent

Read stored observations or forecast history for source and var. Forecast source uses forecast:model and requires spot. Public access covers the latest 72 hours; a free Gillby account (OAuth sign-in) allows windows up to 31 days; up to 1000 points. Latest revision/run wins. Rejected and suspect rows are excluded; unreviewed rows retain quality metadata. This is a stored source archive. source=forecast:* returns model estimates or predictions, even when valid times are in the past; it does not establish observed rainfall. For measured historical daily precipitation use get_rainfall_history and preserve coverage. For chop trends use get_chop_history with recorded/reconstructed provenance and gaps. Units: ft, kt, s, F, mi; directions FROM. Returns data and source/freshness metadata.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "get_history"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 18, 2026

TDQS

A4.8/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the read-only/idempotent profile, but the description adds substantive behavior: 72-hour public window vs 31-day authenticated window, 1000-point cap, latest revision/run precedence, exclusion of rejected/suspect rows, retention of quality metadata on unreviewed rows, and the crucial warning that source=forecast:* yields model values that do not establish observed rainfall.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Content is front-loaded with the core operation and scoping rules, followed by routing guidance and units. It is dense but every sentence contributes; the only cost is that the third paragraph (units/returns) is somewhat compressed, and it is a long single block without visual breaks.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description compensates by noting that data is returned with source/freshness metadata, and it supplies units (ft, kt, s, F, mi) and direction convention (FROM). Combined with access-tier and provenance caveats, an agent has everything needed to call and interpret this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries the full burden, and it does well: it explains source syntax (forecast:model), the spot requirement for forecast sources, the from/to window limits, and the maxPoints ceiling that matches the schema's maximum. It omits explicit enumeration of valid `var` values, leaving a small gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource ('Read stored observations or forecast history for source and var') and immediately distinguishes itself from the measured-history siblings get_rainfall_history and get_chop_history. An agent can tell exactly what class of data this returns without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit routing rules: use get_rainfall_history for measured historical daily precipitation, get_chop_history for chop trends, and this tool for stored source archives including forecast:* estimates. It also states the access tiers and spot requirement, so the agent knows both when to use it and what conditions govern a call.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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